Approximations with evolutionary pursuit
Signal Processing
The fan-chirp transform for non-stationary harmonic signals
Signal Processing
Classification of acoustic emissions using modified matching pursuit
EURASIP Journal on Applied Signal Processing
A new time-frequency transform for non-stationary signals with any nonlinear instantaneous phase
Multidimensional Systems and Signal Processing
Conditional spectral moments in matching pursuit based on the chirplet elementary function
Digital Signal Processing
Time--frequency feature representation using energy concentration: An overview of recent advances
Digital Signal Processing
Matched representations and filters for guided waves
IEEE Transactions on Signal Processing
Time-frequency representation based on an adaptive short-time Fourier transform
IEEE Transactions on Signal Processing
Multiple target tracking in wireless networks based on time-frequency distributions
SENSIG'08 Proceedings of the 1st WSEAS international conference on Sensors and signals
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A new four-parameter atomic decomposition of chirplets is developed for compact and precise representation of signals with chirp components. The four-parameter chirplet atom is obtained from the unit Gaussian function by successive applications of scaling, fractional Fourier transform (FRFT), and time-shift and frequency-shift operators. The application of the FRFT operator results in a rotation of the Wigner distribution of the Gaussian in the time-frequency plane by a specified angle. The decomposition is realized by using the matching pursuit algorithm. For this purpose, the four-parameter space is discretized to obtain a small but complete subset in the Hilbert space. A time-frequency distribution (TFD) is developed for clear and readable visualization of the signal components. It is observed that the chirplet decomposition and the related TFD provide more compact and precise representation of signal inner structures compared with the commonly used time-frequency representations